PyTorch:作为张量操作的动态编程 [英] PyTorch: Dynamic Programming as Tensor Operation

问题描述

是否可以通过Tensor操作获得以下循环?

Is it possible to get the following loop with a Tensor operation?

a = torch.Tensor([1, 0, 0, 0])
b = torch.Tensor([1, 2, 3, 4])

for i in range(1, a.shape[0]):
    a[i] = b[i] + a[i-1]

print(a) # [1, 3, 6, 10]

该操作取决于 a 中的先前值和在途中(以动态编程方式)计算出的值.

The operation depends on the previous values in a and the values that are computed on the way (in a dynamic programming fashion).

是否可以通过张量运算获得这种顺序计算?

Is it possible to get this type of sequential computation with a tensor operation?

推荐答案

您可以使用累计和:

b.cumsum(0)

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